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Objectives <p>Intracranial hemorrhage (ICH) is a time-critical neurological emergency in which rapid CT-based assessment directly informs treatment decisions. This study aimed to develop an automated deep-learning pipeline to enhance ICH detection, segmentation, and localization, complemented by clinical decision-making support through a large language model.</p> Materials and methods <p>The detection model was trained on 21,784 labeled and 3528 unlabeled CT scans from the RSNA dataset using semi-supervised learning. The segmentation model was trained on 1226 scans from the HS dataset to delineate six ICH subtypes. Hydrocephalus and midline-shift models were trained on a dedicated 507-scan subset of the HS dataset. Hemorrhage and edema locations were registered to standard brain regions to improve interpretability. For evaluation, the CQ500 dataset (491 patients) was used as an external validation and test cohort. Clinical recommendations were generated using the GPT-4o Assistants API based on published guidelines and trials.</p> Results <p>On the test set, detection achieved an AUC of 0.96 (95% CI: 0.94–0.98), and segmentation yielded Dice values ranging from 0.71 to 0.93 with corresponding 95% CIs from 0.61–0.76 to 0.90–0.96, while volume estimation showed high concordance (CCC 0.820–0.996). Intraparenchymal hemorrhage (IPH) localization demonstrated strong agreement with κ values of 0.85–1.00 across brain regions. Clinical decisions generated by the pipeline were highly rated, with one neurosurgeon assigning median scores of 4 and 5 for examination and treatment, and the other assigning 5 for both.</p> Conclusions <p>This deep learning pipeline combines imaging analysis with actionable clinical decisions, demonstrating significant potential as a valuable tool for emergency care.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Rapid and accurate identification of ICH on CT is critical for guiding treatment, yet remains difficult using standard emergency radiological evaluation</i>.</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>The end-to-end artificial intelligence pipeline achieved high accuracy in ICH detection, segmentation, and localization, with strong concordance to manual measurements and reliable clinical recommendations</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>By automating image analysis and clinical decision-making, the pipeline demonstrated significant potential to reduce diagnostic delays, improve treatment guidance, and enhance patient outcomes in emergency care settings</i>.</p> Graphical Abstract <p></p>

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Integrated CT pipeline for automatic intracranial hemorrhage evaluation with GPT-enhanced clinical decision support

  • 期刊论文

  • 发表日期：

    2026年1月26日

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  • Xianjing Zhao,
  • Ruxuan Yan,
  • Jianlin Wang,
  • Liting Shi,
  • Kun Chen,
  • Yulong Yang,
  • Hui Xu,
  • Zheng Lin,
  • Bo Chen,
  • Long Liang,
  • Chengting Lin,
  • Rende Wang,
  • Linka Wang,
  • Yifan Cai,
  • Zhenwei Yao,
  • Lei Shi

摘要

Objectives

Intracranial hemorrhage (ICH) is a time-critical neurological emergency in which rapid CT-based assessment directly informs treatment decisions. This study aimed to develop an automated deep-learning pipeline to enhance ICH detection, segmentation, and localization, complemented by clinical decision-making support through a large language model.

Materials and methods

The detection model was trained on 21,784 labeled and 3528 unlabeled CT scans from the RSNA dataset using semi-supervised learning. The segmentation model was trained on 1226 scans from the HS dataset to delineate six ICH subtypes. Hydrocephalus and midline-shift models were trained on a dedicated 507-scan subset of the HS dataset. Hemorrhage and edema locations were registered to standard brain regions to improve interpretability. For evaluation, the CQ500 dataset (491 patients) was used as an external validation and test cohort. Clinical recommendations were generated using the GPT-4o Assistants API based on published guidelines and trials.

Results

On the test set, detection achieved an AUC of 0.96 (95% CI: 0.94–0.98), and segmentation yielded Dice values ranging from 0.71 to 0.93 with corresponding 95% CIs from 0.61–0.76 to 0.90–0.96, while volume estimation showed high concordance (CCC 0.820–0.996). Intraparenchymal hemorrhage (IPH) localization demonstrated strong agreement with κ values of 0.85–1.00 across brain regions. Clinical decisions generated by the pipeline were highly rated, with one neurosurgeon assigning median scores of 4 and 5 for examination and treatment, and the other assigning 5 for both.

Conclusions

This deep learning pipeline combines imaging analysis with actionable clinical decisions, demonstrating significant potential as a valuable tool for emergency care.

Key Points

Question Rapid and accurate identification of ICH on CT is critical for guiding treatment, yet remains difficult using standard emergency radiological evaluation.

Findings The end-to-end artificial intelligence pipeline achieved high accuracy in ICH detection, segmentation, and localization, with strong concordance to manual measurements and reliable clinical recommendations.

Clinical relevance By automating image analysis and clinical decision-making, the pipeline demonstrated significant potential to reduce diagnostic delays, improve treatment guidance, and enhance patient outcomes in emergency care settings.

Graphical Abstract